Abstract: System and methods for automatically providing action recommendations are described. A method may include collecting a set of telemetry data from a client application. The set of telemetry data contains a plurality of pages generated based on a plurality of user actions performed on the client application. The method may include generating a plurality of prior probabilities corresponding to the plurality of pages and the plurality of user actions. In response to the client application displaying a first page, the method may generating a plurality of posterior probabilities for a subset of user actions that can be invoked in the client application from the first page, and selecting a plurality of recommended actions from the subset of user actions for having the highest corresponding posterior probabilities among the plurality of posterior probabilities.

Abstract: In one example, a request to migrate an application running on a first compute node to a second compute node with a change in a version of the application may be received. Migration information may be provided to a cloud-based Migration as a Service provider based on the request. An application migration component may be received from the cloud-based Migration as a Service provider based on the migration information. Further, the application migration component may be executed to migrate the application from the first compute node to the second compute node with the change in the version of the application.

Abstract: In one example, a request to migrate a server application running on a first server to a second server with a change in a version of the server application may be received. Migration information may be provided to a cloud-based Migration as a Service provider based on the request. An application migration component may be received from the cloud-based Migration as a Service provider upon validation of the migration information. The server application may be migrated from the first server to the second server with the change in the version of the server application using the application migration component and the cloud-based Migration as a Service provider. A first version of an agent application in a client may be upgraded with a modified version of the agent application using the application migration component.

Abstract: In one example, a request to migrate a server application running on a first server to a second server with a change in a version of the server application may be received. Migration information may be provided to a cloud-based Migration as a Service provider based on the request. An application migration component may be received from the cloud-based Migration as a Service provider upon validation of the migration information. The server application may be migrated from the first server to the second server with the change in the version of the server application using the application migration component and the cloud-based Migration as a Service provider. A first version of an agent application in a client may be upgraded with a modified version of the agent application using the application migration component.

Abstract: In one example, a request to migrate an application running on a first compute node to a second compute node with a change in a version of the application may be received. Migration information may be provided to a cloud-based Migration as a Service provider based on the request. An application migration component may be received from the cloud-based Migration as a Service provider based on the migration information. Further, the application migration component may be executed to migrate the application from the first compute node to the second compute node with the change in the version of the application.

Abstract: An event-notification system provides for monitoring resource-status parameters so as to repeatedly generate resource-status values for each of N resource-status parameters applicable to the resources. Each datapoint specifies a resource, a time of collection, and the values of parameters for the resource at the time. The datapoints are represented in an N-dimensional space, including N dimensions corresponding to the resource-status parameters and one dimension devoted to time. The datapoints are clustered on a proximity basis. The clusters are manually classified as Normal or Ack-Abnormal. A machine-learning engine develops a model that allows the classifications to be automated. Alerts are sent for respective Abnormal clusters rather than for each abnormal datapoint, thus reducing the amount of data an administrator must deal with to address potential problems.

Abstract: System and methods for automatically providing action recommendations are described. A method may include collecting a set of telemetry data from a client application. The set of telemetry data contains a plurality of pages generated based on a plurality of user actions performed on the client application. The method may include generating a plurality of prior probabilities corresponding to the plurality of pages and the plurality of user actions. In response to the client application displaying a first page, the method may generating a plurality of posterior probabilities for a subset of user actions that can be invoked in the client application from the first page, and selecting a plurality of recommended actions from the subset of user actions for having the highest corresponding posterior probabilities among the plurality of posterior probabilities.

Abstract: An event-notification system provides for monitoring resource-status parameters so as to repeatedly generate resource-status values for each of N resource-status parameters applicable to the resources. Each datapoint specifies a resource, a time of collection, and the values of parameters for the resource at the time. The datapoints are represented in an N-dimensional space, including N dimensions corresponding to the resource-status parameters and one dimension devoted to time. The datapoints are clustered on a proximity basis. The clusters are manually classified as Normal or Ack-Abnormal. A machine-learning engine develops a model that allows the classifications to be automated. Alerts are sent for respective Abnormal clusters rather than for each abnormal datapoint, thus reducing the amount of data an administrator must deal with to address potential problems.